Symbolic regression is a promising technique for analyzing and identifying relationships within complex and/or expansive data sets. Typically, data sets being analyzed can include physically observable numerical data or data derivable from real world phenomenon. Complex systems yield correspondingly complex and expansive data sets. Symbolic regression provides one avenue on which to attempt to tackle analysis of such data sets. Symbolic regression can include a function discovery approach for analysis and modeling of numeric multivariate data sets. Insights about data and data generating systems can be determined by discovering a variety of symbolic expressions of functions that fit a given data set.
Symbolic regression, as opposed to classical regression techniques, enables discovery of both the form of the model and its parameters. Conventional implementations of symbolic regression systems proceed by asking a user to select a set of primitive functional operators allowed to generate mathematical models to evaluate data sets and then by applying learning algorithms to derive model structures and model parameters. However, even symbolic regression analysis can require a user to specify what expressions to look for. The need for a human user can be a severe bottleneck even in symbolic regression settings.